AI in Production

Definition

What is artificial intelligence?

Artificial intelligence (AI) refers to the ability of machines or computers to perform tasks with human-like intelligence. This includes learning from experience, making decisions, recognizing correlations and independently solving complex challenges.

While traditional computer programs are based on explicit rules and predefined instructions, AI applications work on the basis of algorithms that are able to learn from data and improve themselves.

Combined with the processing capacities of modern computers, AI applications are able to solve tasks within a very short time that traditionally require human intelligence and either cannot be done manually or require substantial time investment. Examples include dealing with human language, recognizing patterns in vast volumes of data and predicting future developments.

Use Cases

Use cases for AI in production

AI applications offer great potential for manufacturing. This is partly due to the fact that large amounts of data are produced in complex production environments – e.g. by machines and sensors that continuously generate data on processes, products or environmental parameters. AI applications offer new opportunities to use this data profitably and ultimately realize both cost savings and competitive advantages such as faster and more flexible production or higher product quality.

Advances in data processing and storage, particularly through cloud technologies, are supporting this development.

Below we present four use cases for artificial intelligence in production:

• Predictive Maintenance
• Quality inspection and error detection
• Optimized production planning and decision-making
• Advanced production monitoring

Predictive Maintenance

In predictive maintenance, data from sensors and other sources are analyzed to identify maintenance needs and predict the optimal maintenance timing.

This approach ensures that maintenance tasks are performed promptly, reducing unplanned downtimes and extending the lifespan of equipment. Additionally, it helps avoid unnecessary maintenance costs and allows for better coordination of maintenance with personnel planning.

AI analysis models can detect patterns in large datasets that indicate wear or malfunctions, taking numerous variables into account.

AI-based predictive maintenance models learn from historical data on equipment usage and can be continuously improved over time to provide increasingly accurate predictions of performance, wear, and failures.

To develop an AI model for predictive maintenance, the required (historical) data must first be collected and preprocessed. Data often originates from sensors that collect information such as temperature, vibration, or pressure readings. The next step involves selecting suitable algorithms. The model is then trained using the collected data. Validation methods ensure that the resulting model can make accurate predictions.

Quality inspection and error detection

AI solutions also enable significant improvements in ensuring and optimizing product quality in many areas.

A key application is the automation of visual inspections. Cameras and image processing algorithms are used to detect product defects, often with greater accuracy than human inspectors. The algorithms used here are trained using appropriate data.

Specific use cases for AI-based image recognition systems include inspecting circuit boards for short circuits or missing components, checking packaging in pharmaceuticals, and sorting food products.

Another advantage of AI solutions in quality assurance is their ability to correlate numerous data points to assess or predict product quality. This can involve analyzing sensor data as well as process and usage-related data from sources such as machine controls or software systems, which may indicate quality deficiencies.

Optimized production planning and decision-making

Production planning involves taking numerous factors into account – especially in more complex production environments.

AI solutions can significantly support planning processes by searching through large datasets, analyzing them, and providing information on order status, machine utilization, and available resources.

When it comes to data preparation, artificial intelligence offers the potential for substantial efficiency gains. Instead of manually searching complex data structures and creating reports, AI applications can often provide the necessary correlations by combining data from various sources without delay.

Retrieval Augmented Generation integrates existing large language models (LLMs) with selected data sources such as Web APIs and proprietary databases. This allows companies to utilize existing models that have been trained on large amounts of data, while at the same time ensuring that AI applications provide up-to-date and contextualised information. An example use case is the manubes Chat Assistant, an AI tool that answers questions about stored production data.

Additionally, autonomous forecasts or recommendations from AI tools (e.g., predictions of future demand and resource requirements based on historical data) can be integrated into business decision-making.

With the help of specialized software solutions, many planning processes can be fully automated.

Advanced production monitoring

The constant monitoring of production processes is already a central task in production control. Control teams use real-time dashboards with production metrics and process data visualizations to identify disruptions and other problems as quickly as possible.

Artificial intelligence supports production monitoring with the ability to analyze huge amounts of data and identify relevant patterns. While traditional production monitoring involves monitoring selected areas and parameters, AI can also deal with unstructured data.

Real-time data from sensors, machine controls and software systems can be combined to uncover complex correlations. These can not only indicate potential problems in production, but also serve as a basis for long-term production optimization.

Example

AI-based data insights in production – manubes Chat Assistant

The AI-based chat assistant of our cloud platform manubes is an example for the seamless integration of AI tools into existing software solutions for manufacturers.

manubes allows companies in the manufacturing and process industries to aggregate, store, structure and visualize production data using a central platform. With the help of automated workflows, processes can be automated while establishing effective and secure interfaces for mobile production control.

The AI-powered chat assistant allows manubes users to access their production data faster and more efficiently. It analyzes existing data models to provide detailed information on orders, ressources, disruptions and many other aspects of production within seconds.

These enhanced capabilities for data access and analysis help companies realize significant time savings for data preparation, gain access to advanced insights and ensure that decisions aren’t being slowed down due to missing data.

As an innovative cloud platform for digital production management, manubes offers a scalable and secure infrastructure for the use of AI tools. The manubes Chat Assistant integrates into the platform seamlessly and opens up new possibilities in production planning, control and monitoring.

Do you want to try the manubes Chat Assistant yourself? Create your free test account and get access to all manubes features in your personal cloud environment.